Klaviyo to Panoply

This page provides you with instructions on how to extract data from Klaviyo and load it into Panoply. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Klaviyo?

Klaviyo is an ecommerce marketing automation platform that lets organizations run data-driven email and Facebook campaigns.

What is Panoply?

Panoply provides a managed data warehouse platform that lets users quickly set up a new Amazon Redshift instance. It uses machine learning algorithms to handle complex tasks like schema building, data mining, modeling, scaling, performance tuning, security, and backup. Panoply can import data with no schema, no modeling, and no configuration, and you can work with the analysis, SQL, and visualization tools you already know on data in Panoply just as you would if you were creating a Redshift data warehouse manually.

Getting data out of Klaviyo

Klaviyo exposes data through several REST APIs, which developers can use to extract information on metrics, profiles, lists, campaigns, and templates. Each of these APIs has two to seven optional parameters you can use to refine the information returned. As an example, a simple call to retrieve data via the Klaviyo Metrics API would look like:

GET https://a.klaviyo.com/api/v1/metrics

The GET call returns a JSON object with all the fields of the specified dataset as a reply. All fields may not be present for any given record. The JSON might look like:

Loading data into Panoply

When you've identified all the columns you want to insert, use the Reshift CREATE TABLE statement to make a table in your data warehouse to receive the data.

Now you can replicate your data. It may seem as if the easiest way to do that (especially if there isn't much of it) is to build INSERT statements and add data to your table row by row. If you have any experience with SQL, this probably will be your first inclination. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should instead load the data into Amazon S3 and then use the Redshift COPY command to import it into Redshift.

Keeping Klaviyo data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Klaviyo.

And remember, as with any code, once you write it, you have to maintain it. If Klaviyo modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

Other data warehouse options

Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, and To Azure SQL Data Warehouse.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to move data from Klaviyo to Panoply automatically. With just a few clicks, Stitch starts extracting your Klaviyo data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Panoply data warehouse.